MedComm - Future medicine最新文献

In the recent article published in Cell [1], Kaltenecker et al. systematically uncovered the liver as an active pathological driver of cancer cachexia (CCx). Through integrated multi-omics analysis, the study identifies the downregulation of the circadian regulator REV-ERBα (nuclear receptor subfamily 1 group D member 1, NR1D1) as a central mechanism that disrupts hepatic homeostasis and induces abnormal secretion of hepatokines, thereby promoting peripheral tissue wasting. These findings provide a conceptual framework for developing therapeutic strategies targeting the “liver–peripheral tissue” axis in CCx.

Cancer cachexia is a highly catabolic metabolic syndrome associated with advanced malignancies, characterized by progressive weight loss, skeletal muscle atrophy, and adipose tissue degradation [2, 3]. It is particularly prevalent in pancreatic, gastric, and lung cancers, affecting 50%–80% of patients and accounting for approximately 20% of cancer-related deaths. While previous studies have predominantly focused on local mechanisms of muscle atrophy, such as the ubiquitin-proteasome system (UPS), autophagy, and the NF-κB or FOXO pathways, the systemic metabolic functions of the liver in CCx pathogenesis remain underexplored. Notably, a concurrent study by Garrett et al. published in Cell demonstrated that tumor-driven inflammation can alter the vagal nerve–liver axis, resulting in suppression of hepatic HNF4α and exacerbation of systemic catabolism, further highlighting the critical involvement of the liver in this condition [4].

Given the pivotal role of the liver in systemic metabolism, the authors hypothesized that tissue wasting in cachexia may originate from hepatic responses to tumor-derived signals, which drive peripheral tissue catabolism through the secretion of specific factors. To investigate this, Kaltenecker et al. performed integrated transcriptomic and epigenomic profiling of hepatocytes from both weight-stable and cachectic cancer mouse models. These analyses uncovered a distinct transcriptional program specific to cachexia, characterized by dysregulated expression of the circadian regulator REV-ERBα. Hepatic restoration of REV-ERBα expression markedly attenuated both muscle and fat wasting. Mechanistically, REV-ERBα modulates tissue degradation by controlling hepatokines that activate catabolic pathways in myotubes and adipocytes. Notably, clinical data further revealed elevated circulating levels of key hepatokines in cachectic patients, independent of classical inflammatory cytokines such as IL-6 or TNF-α, underscoring a novel “hepatokine-peripheral tissue” axis in CCx pathophysiology.

Delving further into molecular effectors, the authors identified three key liver-secreted proteins-lipopolysaccharide-binding protein (LBP), inter-α-trypsin inhibitor heavy-chain H3 (ITIH3), and insulin-like-growth-factor-binding protein 1 (IGFBP1)–as direct targets of REV-ERB

Tumor immunogenicity determines their response to immune checkpoint inhibitors (ICIs), but the mechanisms governing pancancer immunogenicity remain incompletely understood. A further critical barrier to developing reliable predictive biomarkers is data set shift, which undermines model generalizability. Here, we address these challenges by developing a novel adversarial validation (AV)-integrated machine learning framework, focusing on immunogenic cell death (ICD)-related gene signatures (ICDRSs). We designed three AV-based strategies to mitigate data set shift and validate the efficacies across multiple machine learning algorithms. Using dual-modal data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), four optimal AV-based classifiers (e.g., GradientBoosting, XGBoost, LGBM, and CatBoost) were screened, which effectively reduced inter-cohort shift, enhancing both accuracy and robustness of downstream analysis. We identified novel risk/protective ICDRSs that strongly predicted patient survival and tumor immunogenicity across cancers. High-risk ICDRSs correlated with immune-exclusive microenvironments marked by impaired antigen presentation and aberrant tumor-associated macrophage development, as revealed by single-cell RNA sequencing. Validation across 13 ICI-treated cohorts revealed the capacity of ICDRSs for anti-PD-1 nonresponse. Mechanistically, risk ICDRSs were linked to CD47-SIRPA-mediated immune evasion and proliferative macrophage subsets with terminal dysfunction. This study advances understanding of tumor immunogenicity, provides novel biomarker development tools, and supports personalized cancer immunotherapy decision-making.

Given that the retina shares embryonic origin with the central nervous system, past evidence has attempted to prove parallel pathology of neurovegetative diseases in the retina. Retinal imaging techniques provide in vivo structural and functional data with advantages of high resolution and low cost in a noninvasive way. In recent literature, the retina presents significant alterations related to Parkinson's disease (PD) and other neurodegenerative diseases through multimodal retinal images due to its neural accessibility. However, current findings remain fragmented and inconsistent causing the existing reviews limited in a certain modality or incomplete without mention of interdisciplinary integration. The purpose of this paper is to systematically review and synthesize the application of retinal imaging techniques in PD and other neurodegenerative diseases. We begin with the connections between the retina and the brain and the main retinal imaging modalities. We then summarize the retinal changes in patients with PD and other neurodegenerative diseases during the past decade. Additionally, we discuss the application of artificial intelligence in PD prediction and retinal probe. By integrating histopathological insights with advanced imaging analytics, we highlight retinal changes as biomarkers for neurodegeneration, which accelerate their clinical translation for early diagnosis and monitoring of PD in the future. and other neurodegenerative diseases.

Tumor-targeted radioimmunotherapy (RIT) has the dual capability of delivering ionizing radiation to cancer cells while modulating the tumor microenvironment (TME) to enhance immune responses. These immune-stimulatory properties suggest that RIT could synergize with PD-1 blockade. However, the precise immune mechanisms underlying this potential synergy remain unclear. Here we show that ¹⁷⁷Lu-DOTA-M5A, the radiolabeled antibody against carcinoembryonic antigen (CEA), induces tumor regression and alters the TME when combined with PD-1 blockade in a colorectal cancer (CRC) model. Using in vitro uptake assays and in vivo studies in CEA-transgenic mice, we found that low-dose ¹⁷⁷Lu-DOTA-M5A (2.5 MBq) combined with anti-PD-1 achieved complete tumor control, with −6% growth rate, in contrast to limited efficacy from either monotherapy. This combination extended survival by more than 300% compared to controls, with no median survival reached. Remarkably, this effect was equivalent to that of high-dose monotherapy (5 MBq), indicating a potent synergistic interaction. Immune profiling revealed that RIT altered lymphocyte infiltration, while the combination therapy shifted tumor-associated macrophages toward a pro-inflammatory phenotype. These immune-modulating effects occurred without inducing myelotoxicity. Our findings suggest that PD-1 blockade potentiates the therapeutic efficacy of ¹⁷⁷Lu-DOTA-M5A, supporting its development as a safe and effective combination strategy for CRC therapy.

Bioelectrical impedance technology (EIT) is a promising noninvasive tool for real-time monitoring and diagnosis, especially in neurology. It is gaining attention for its ability to assess the electrical properties of tissues, providing valuable insights into neurological conditions such as stroke, traumatic brain injury, and brain edema. Despite its potential, challenges remain, including limitations in spatial resolution, difficulties in imaging deep brain structures, and the need for standardized protocols across clinical settings. This review explores recent advances in EIT, focusing on its application in neurological disease diagnosis and monitoring. It highlights the integration of advanced algorithms, multimodal imaging, and artificial intelligence (AI) to enhance resolution, efficiency, and clinical applicability. Additionally, the potential for personalized medicine through continuous, real-time monitoring is discussed, along with the need for further research to address existing limitations. This review synthesizes current knowledge and offers insights into future directions for the development and clinical translation of EIT in neurology. It provides a comprehensive overview of EIT's current capabilities and future prospects for improving neurological disease diagnosis and management.

Circulating tumor cells (CTCs) are malignant cells that detach from primary or metastatic tumors and enter the bloodstream. Organoids, as three-dimensional in vitro models, can mimic the tumor microenvironment and histopathological characteristics, thereby serving as valuable tools in tumor research. CTC-derived organoids retain tumor heterogeneity and metastatic potential, which provides a unique model for the study of metastatic cascade mechanisms, individualized drug screening, and precision therapy. However, the current research on CTC-derived organoids faces challenges, such as the scarcity of CTCs and the high technical difficulty in their isolation and enrichment, which leads to a low success rate in constructing organoid models. Moreover, most existing studies focus on a single cancer type and lack systematic integration of full-process standardization as well as cross-cancer applicability. In this paper, we review the isolation and enrichment strategies of CTC-derived organoids along with the techniques for optimizing in vitro culture systems, and discusses their potential applications. This review summarizes the existing results, analyzes the technical bottlenecks, and provides a theoretical basis for the standardized construction and application of CTC-derived organoids, while promoting their application in tumor precision medicine.

Exosomes, nanoscale vesicles secreted by diverse cell types, serve as critical mediators of intercellular and interorgan communication in metabolic physiology. Their unique advantages include encapsulating cell-specific biomolecules that reflect cellular origins, enabling noninvasive liquid biopsy for early disease detection through distinct signatures (e.g., miRNA profiles), and functioning as biocompatible drug delivery platforms or bioactive therapeutics in preclinical models. However, despite their transformative potential in metabolic disease diagnostics and therapy, a systematic synthesis of recent advances, molecular mechanisms, and clinical translation challenges is lacking. To address this gap, this review synthesizes cutting-edge insights into exosome biology—spanning composition, biogenesis, secretion, and tissue-specific roles in adipose, liver, muscle, and pancreas—and critically evaluates their dual diagnostic–therapeutic applications across obesity, diabetes, nonalcoholic fatty liver disease (NAFLD), and associated complications. We further delineate key translational hurdles (e.g., production scalability, cargo heterogeneity, and clinical validation) and propose strategies for standardization. By integrating interdisciplinary advances from nanotechnology, omics, and artificial intelligence (AI), this work provides a foundational framework to accelerate the clinical implementation of exosome-based approaches, ultimately advancing precision medicine for metabolic disorders.

Accurate anatomical variant detection is critical in clinical diagnostics, yet disparities in imaging modalities often challenge reliable assessment. In dentistry, panoramic radiographs (PRs) are widely used for mandibular canal evaluation, but their reported detection rates for bifid variants (0.038%–1.98%) fall far below those of cone-beam computed tomography (CBCT; 10%–66%), highlighting a need for improved diagnostic tools. Here, we address this gap by developing a deep learning-based tri-comparison expertise decision (TED) system to automate mandibular canal variant classification on PRs. Using retrospective data from 442 mandible sides (279 participants, aged 18–32 years), we validated PRs against CBCT ground truth and decomposed multi-class classification into pairwise comparisons with an “Another” class to enhance discrimination of anatomically similar variants. Here we show that the TED system achieved superior diagnostic accuracy (0.701, 95% CI: 0.674–0.728) and AUROC (0.854, 95% CI: 0.824–0.884) compared to assessments by five experienced dentists (highest accuracy: 0.683; AUROC: 0.810), while also revealing strikingly low inter-rater agreement among experts (Fleiss' kappa = 0.046). These results demonstrate that the TED approach not only outperforms manual evaluations but also provides consistent, cost-effective automation of a task prone to human variability. By bridging the performance gap between PRs and CBCT, this tool offers a practical solution for preoperative risk assessment in dental practice. Broader validation across diverse clinical settings could further solidify its role in improving diagnostic workflows and patient outcomes.

Zebrafish (Danio rerio) have gained prominence as a model organism in biomedical research due to its genetic accessibility, optical transparency during embryonic development, and physiological similarities to humans. These traits make zebrafish ideal for studying various human diseases, though broader insights into their diverse applications are still needed. This review explores zebrafish as a versatile model for studying both communicable and non-communicable diseases. In communicable diseases, it has become a powerful model for studying host–pathogen interactions, immune responses, and therapeutic screening, with its transparency enabling real-time tracking of infections. Robust models also exist for many bacterial, viral, and fungal pathogens, supported by early innate immune cell development. Additionally, microinjection techniques enable precise local or systemic infections, making zebrafish a versatile, high-resolution model for studying disease mechanisms. For non-communicable diseases, zebrafish support research on cardiovascular, metabolic, neurodegenerative disorders, and cancer. This review highlights recent advances in using zebrafish to study disease mechanisms, drug discovery, and therapies. It underscores the academic and translational value of zebrafish, promoting innovative strategies to improve human health outcomes. Their versatility across disciplines makes them an effective tool for both fundamental research and biomedical education, positioning them as a bridge between basic science and clinical applications.